Automatically determining size or shape of a radiation beam

ABSTRACT

Systems and methods for automatically determining a beam parameter at each of a plurality of treatment nodes are disclosed. The beam parameter may include a beam shape, beam size and/or beam orientation. Systems and methods for automatically selecting multiple collimators in a radiation treatment system are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 60/790,503, filed on Apr. 7, 2006, the entirety of which is herebyincorporated by reference.

FIELD

Embodiments of the present invention relate generally to radiationtreatment and, more particularly, to treatment planning in radiationtreatment.

BACKGROUND

Tumors and lesions are types of pathological anatomies characterized byabnormal growth of tissue resulting from the uncontrolled, progressivemultiplication of cells, while serving no physiological function.Pathological anatomies can be treated with an invasive procedure, suchas surgery, but this can be harmful and full of risks for the patient. Anon-invasive method to treat a pathological anatomy (e.g., tumor,legion, vascular malformation, nerve disorder, etc.) is external beamradiation therapy. In one type of external beam radiation therapy, anexternal radiation source is used to direct a sequence of x-ray beams ata tumor site from multiple angles, with the patient positioned so thetumor lies in the path of the beam. As the angle of the radiation sourcechanges, every beam passes through the tumor site, but passes through adifferent area of healthy tissue on its way to the tumor. As a result,the cumulative radiation dose at the tumor is high and the averageradiation dose to healthy tissue is low.

The term “radiotherapy” refers to a radiation treatment procedure inwhich radiation is applied to a target region for therapeutic, ratherthan necrotic, purposes. The amount of radiation utilized inradiotherapy treatment sessions is typically about an order of magnitudesmaller, as compared to the amount used in a radiosurgery session.Radiotherapy is typically characterized by a low dose per treatment(e.g., 100-200 centigray (cGy)), short treatment times (e.g., 10 to 30minutes per treatment) and conventional or hyperfractionation (e.g., 30to 45 days of treatment). For convenience, the term “radiationtreatment” is used herein to mean radiosurgery and/or radiotherapyunless otherwise noted by the magnitude of the radiation.

In order to deliver a requisite dose to a targeted region, whilstminimizing exposure to healthy tissue and avoiding sensitive criticalstructures, a suitable treatment planning system is required. Treatmentplans specify quantities such as the directions and intensities of theapplied radiation beams, and the durations of the beam exposure. It isdesirable that treatment plans be designed in such a way that aspecified dose (required for the clinical purpose at hand) be deliveredto a tumor, while avoiding an excessive dose to the surrounding healthytissue and, in particular, to any important nearby organs. Developing anappropriate treatment planning system is especially challenging fortumors that are larger, have irregular shapes, or are close to asensitive or critical structure.

A treatment plan may typically be generated from input parameters suchas beam positions, beam orientations, beam shapes, beam intensities, anddesired radiation dose constraints (that are deemed necessary by theradiologist in order to achieve a particular clinical goal).Sophisticated treatment plans may be developed using advanced modelingtechniques, and state-of-the-art optimization algorithms.

Two kinds of treatment planning procedures are known: forward planningand inverse planning. In the early days of radiation treatment,treatment planning systems tended to focus on forward planningtechniques. In forward treatment planning, a medical physicistdetermines the radiation dose duration, or beam-on time, and trajectoryof a chosen beam and then calculates how much radiation will be absorbedby the tumor, critical structures (i.e., vital organs) and other healthytissue. There is no independent control of the dose levels to the tumorand other structures for a given number of beams, because the radiationabsorption in a volume of tissue is determined by the properties of thetissue and the distance of each point in the volume to the origin of thebeam and the beam axis. More specifically, the medical physicist may“guess” or assign, based on his experience, values to various treatmentparameters such as beam positions and beam intensities. The treatmentplanning system then calculates the resulting dose distribution. Afterreviewing the resulting dose distribution, the medical physicist mayadjust the values of the treatment parameters. The system re-calculatesa new resulting dose distribution. This process may be repeated, untilthe medical physicist is satisfied by the resulting dose distribution,as compared to his desired distribution. Forward planning tends to relyon the user's ability to iterate through various selections of beamdirections and dose weights, and to properly evaluate the resulting dosedistributions. The more experienced the user, the more likely that asatisfactory dose distribution will be produced.

Forward planning often utilizes an isocentric treatment process in whichan external radiation source is used to direct a sequence of x-ray beamsat a tumor target from multiple angles, with the patient beingpositioned so the tumor is at the center of rotation (isocenter) of thebeams. In isocentric planning, each available beam is targeted at thesame point to form the “isocenter,” which generally may be a roughlyspherical isodose region as represented by a sphere. Accordingly,isocentric planning may be often applied when treating a tumor that hasa substantially regular (e.g., spherical) shape. The radiation beams areshaped by a device called a collimator. The collimator consists of densematerial that is opaque to radiation, with the exception that there is ahollow portion through which radiation may pass. The shape and size ofthe radiation beam is then determined by the shape and size of thishollow portion (aperture). When we refer to “collimator size”, we meanthe size of radiation beam created by a given collimator configuration,as measured at a given distance from the radiation source. Hence thesize of the sphere of radiation dose in isocentric planning may dependon the collimator size which may be, for example, about 30 millimetersas measured at about 800 millimeters from the radiation source. As theangle of the radiation source is changed, every beam passes through thetumor, but may pass through a different area of healthy tissue on itsway to the tumor. To treat a target pathological anatomy, multiple dosespheres are superimposed or “stacked” on each other in an attempt toobtain a contour that closely matches the silhouette of the pathologicalanatomy. By stacking isocenters within a target volume, a plan may bedeveloped that ensures that nearly all the target receives a sufficientdose. As a result, the cumulative radiation dose at the tumor may behigh and the average radiation dose to healthy tissue may be low.

In gantry-based radiation treatment systems, the radiation beam may beshaped by a multileaf collimator (MLC), to conform to the silhouette ofthe target as seen from the orientation of the radiation beam source.The MLC is mounted on a gantry and coupled to a linear accelerator. TheMLC includes several adjustable leaves which are able to block and/orfilter radiation to vary the beam intensity and control distribution ofthe radiation. The leaves are typically made of a dense material (e.g.,tungsten) that is essentially opaque to radiation, and are mechanicallydriven, individually, in and out of the radiation field of the beam tocreate a radiation field shape. FIG. 1 shows the leaves of an MLCadjusted to create a radiation field shape corresponding to a targetsilhouette. There are two conventional ways in which radiation treatmentplans are generated for MLCs.

Most radiation delivery systems make use of a circular gantrysurrounding the patient with a linear accelerator free to rotate withinthe circle. Multiple beams may be produced moving the accelerator aroundthe circle; the trajectory of the beam can be characterized by a singleangle describing the angle of rotation, called the “gantry angle”. Withconventional IMRT (Intensity Modulated Radiation Therapy) systems havingan MLC, treatment planning is performed by, first, determining anoptimal dose distribution at each node of the treatment system, i.e.each desired angle. After the dose distribution has been determined,field shapes are generated using a leaf sequencing algorithm, takinginto account constraints of the MLC. That is, a set of instructions isgenerated to move the leaves in a given pattern, in order to achieve asclosely as possible the optimum dose distribution. After the predicteddose distribution is calculated from the generated leaf sequencingalgorithm, the radiation treatment of the target volume of interest(“VOI”) occurs.

With conventional 3D conformal systems having an MLC, treatment planningis performed by first matching the leaves of the MLC to the targetsilhouette. In this case, there is no leaf sequencing algorithm, so theplanning component seeks only to match the shape of each beam to thesilhouette of the target from that gantry angle. Once the MLC positionshave been determined, a predicted dose distribution may be generated,and the radiation treatment of the target VOI occurs.

Another mode of delivering radiation treatment is that provided by theCyberKnife® system. Instead of moving the radiation delivery device in acircle around the patient, it is mounted on a multi-jointed roboticmanipulator that has freedom to make both translational and rotationalmovement. Hence, radiation may be delivered from a wide range ofpositions and orientations relative to the patient, instead of beingrestricted to angles chosen within the circular arc on which thegantry-mounted linac can travel.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which:

FIG. 1 is a plan view of a multileaf collimator adjusted to conform to apathological anatomy;

FIG. 2 is a schematic of a graphical output of a treatment planningsoftware displaying a slice of a CT image;

FIG. 3 is a graph showing an ideal DVH for a pathological anatomy;

FIG. 4 is a graph showing a desirable DVH for a critical region;

FIG. 5 is a perspective view of a radiation treatment system havingspatial nodes in accordance with one embodiment of the invention;

FIG. 6 is a perspective view of a collimator at different orientationsin accordance with one embodiment of the invention;

FIG. 7A is a flow chart of one implementation of a treatment planningalgorithm;

FIG. 7B is a flow chart of one implementation of a treatment planningalgorithm in accordance with one embodiment of the invention;

FIG. 7C is a flow chart showing pre-optimization at spatial nodes inaccordance with one embodiment of the invention;

FIGS. 8A-8K are schematic views illustrating pre-optimization algorithmsin accordance with embodiments of the invention;

FIGS. 9A-9B are schematic views illustrating pre-optimization algorithmsin accordance with embodiments of the invention;

FIGS. 10A-10B are schematic views illustrating pre-optimizationalgorithms in accordance with embodiments of the invention;

FIGS. 11A-E are screen shots of a user interface corresponding to atreatment planning algorithm in accordance with one embodiment of theinvention;

FIG. 12 is a perspective view of a non-isocentric radiation beamdelivery at a pathological anatomy in accordance with one embodiment ofthe invention;

FIG. 13 is a block diagram of a system for diagnostic imaging and/ortreatment delivery in accordance with one embodiment of the invention;and

FIG. 14 is a perspective view of a system for diagnostic imaging and/ortreatment delivery in accordance with one embodiment of the invention.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be evident, however, toone skilled in the art that the present invention may be practicedwithout these specific details. In other instances, well-known circuits,structures, and techniques are not shown in detail or are shown in blockdiagram form in order to avoid unnecessarily obscuring an understandingof this description.

Reference in the description to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the invention. The appearances of the phrase “in one embodiment” invarious places in the specification do not necessarily all refer to thesame embodiment.

An apparatus and method for automating the selection of one or moreradiation beam parameters for a radiation treatment system aredescribed. In one particular embodiment, the apparatus and methodautomatically selects a beam size. In another embodiment, the apparatusand method automatically determines the beam shape. In still anotherembodiment, the apparatus and method automatically determines the beamorientation. It will be appreciated that the apparatus and method mayautomatically determine combinations of the beam size, beam shape andbeam orientation. Embodiments of the apparatus and method may alsoautomatically select multiple collimators. Embodiments of the apparatusand method may also automatically select one or more collimators basedon the automatically determined beam parameter(s).

In inverse planning, in contrast to forward planning, the medicalphysicist specifies a desired dose distribution, for example, theminimum dose to the tumor and the maximum dose to other healthy tissues,independently, and the treatment planning module then selects thedirection, distance, and total number and intensity of the beams inorder to achieve the specified dose conditions. Given a desired dosedistribution specified and input by the user (e.g., the minimum andmaximum doses), the inverse planning module selects and optimizes doseweights and/or beam directions, i.e. selects an optimum set of beamsthat results in such a distribution.

During inverse planning, volumes of interest (VOIs) are used torepresent user-defined structures to be targeted or avoided with respectto the administered radiation dose. That is, the radiation source ispositioned in a sequence calculated to localize the radiation dose intoa VOI that represents the tumor requiring treatment, while as much aspossible avoiding radiation dose to VOIs representing criticalstructures. Once the target (e.g., tumor) VOI has been defined, and thecritical VOIs and soft tissue (all tissue within the treatment regionthat is represented by neither a target nor critical VOI) volumes havebeen specified, the responsible radiation oncologist or medicalphysicist specifies, for example, the minimum radiation dose to thetarget VOI and the maximum dose to normal and critical healthy tissue.The software then produces the inverse treatment plan, relying on thepositional capabilities of the radiation treatment system, to meet thedose constraints of the treatment plan.

FIG. 2 is a conceptual illustration of a graphical output of a treatmentplanning system displaying a slice of a CT image. The illustration ofthe CT image includes a pathological anatomy that is targeted fortreatment, as well as a critical region that is positioned near thepathological anatomy. The treatment planning software enables thegeneration of a critical region contour around the critical region and atarget region contour around the pathological anatomy. Conventionally, auser manually delineates points (e.g., some of the dots on the contourlines of FIG. 2) on the display that are used by the treatment planningsoftware to generate the corresponding contours. While this may seem aneasy task, such matching is difficult due to the three-dimensionalnature and irregularities of pathological and normal anatomies. Based onspecified minimum dose to the target region and the maximum dose to thecritical region, the treatment planning software generates the doseisocontour for the target region. The dose isocontour is a line ofconstant dose, and represents either a given dose percentage (e.g., 60%,70%, 80%, etc.) of a specified prescription dose for the target region,or an absolute dose value (e.g. 2000 centiGray). Ideally, the doseisocontour representing the minimum amount of dose deemed to beclinically effective should perfectly match the contour of the targetregion. In some cases, the dose isocontour generated by the treatmentplanning software is not optimal, and can include portions of thecritical region, as illustrated in FIG. 2.

Two of the principal requirements for an effective radiation treatmentsystem are homogeneity and conformality. Homogeneity is the uniformityof the radiation dose over the volume of the target (e.g., pathologicalanatomy such as a tumor, lesion, vascular malformation, etc.) and can becharacterized by a dose volume histogram (DVH). The DVH represents, onthe y axis, a volume, either as an absolute measurement or a percentageof the VOI volume. On the x axis are dose values, either as absolutedose or as percentage of a given dose (e.g. maximum dose or prescriptiondose). The DVH graph shows how much volume of the VOI is covered by adose greater than or equal to the corresponding dose value on the xaxis. An ideal DVH for the pathological anatomy would be a rectangularfunction as illustrated in FIG. 3, where the dose is 100 percent of theprescribed dose over the volume of the pathological anatomy. A desirableDVH for a critical region would have the profile illustrated in FIG. 4,where the volume of the critical anatomical structures receives aslittle of the prescribed dose as possible.

Conformality is the degree to which the radiation dose matches (conformsto) the shape and extent of the target (e.g., tumor) in order to avoiddamage to critical adjacent structures. More specifically, conformalityis a measure of the amount of prescription (Rx) dose (amount of doseapplied) within a target VOI. Conformality may be measured using aconformality index (CI)=total volume at ≧Rx dose/target volume at ≧Rxdose. Perfect conformality results in a CI=1. With conventionalradiotherapy treatment, using treatment planning software, a clinicianidentifies a dose isocontour for a corresponding VOI for application ofa treatment dose (e.g., 3000 cGy).

A goal of radiation treatment planning is to find a set of radiationbeams including the position, shape, and “weight” (amount of radiationdelivered by the beam) of each beam that produces a dose distributionthat matches clinical objectives (such as minimum and maximum dose totarget and critical structures, conformality, and homogeneity). In arobotic-based radiation treatment such as the CyberKnife® system, theradiation beam can be moved to a variety of positions and orientationsrelative to the patient.

FIG. 5 is a perspective view of a workspace of a radiation treatmentdelivery system 100 including a set of spatial nodes at which toposition the radiation source, in accordance with an embodiment of theinvention. The illustrated embodiment of radiation treatment deliverysystem 100 includes a radiation source 105, a treatment couch 110,detectors 115A and 115B (collectively 115, also referred to as imagers),imaging sources 120A and 120B (collectively 120), and a robotic arm 125.

Radiation treatment delivery system 100 may be used to perform radiationtreatment (e.g., radiosurgery and/or radiotherapy) to treat or destroy alesion (e.g., tumor tissue) within a patient. During radiationtreatment, the patient rests on treatment couch 110, which is maneuveredto position a volume of interest (“VOI”) describing a target to a presetposition or within an operating range accessible to radiation source 105(e.g., field of view). In one embodiment, radiation treatment deliverysystem 100 is an image guided radiation treatment delivery system.Together, imaging sources 120 and detectors 115 are an imaging guidancesystem that provides visual control over the position of treatment couch110 and the patient thereon and the alignment of radiation source 105with respect to the VOI within the patient. In one embodiment, treatmentcouch 110 may be coupled to a positioning system (not illustrated), suchas a robotic arm, that receives feedback from the imaging guidancesystem to provide accurate control over both the displacement andorientation of the VOI within the patient relative to radiation source105.

In one embodiment, robotic arm 125 has multiple (e.g., six) degrees offreedom capable of positioning radiation source 105 with almost aninfinite number of possibilities within its operating envelope. Allowingthis type of movement would result in several challenges. Firstly, alarge number of positional possibilities creates a difficult problem tosolve for a treatment planning system when determining beam positionsand trajectories for treating a particular VOI. Secondly, allowingunconstrained movement within the operating envelope of robotic arm 125may result in possible collisions between radiation source 105 and thepatient or other stationary objects. These problems may be solved bylimiting radiation source 105 to a finite number of spatial nodes fromwhich radiation source 105 may emit a radiation beam and furthercreating specific paths (known safe paths) that robot arm 125 mustfollow between the spatial nodes.

A collection of spatial nodes and associated safe paths interconnectingthese spatial nodes is called a “workspace” or “node set”. FIG. 5illustrates a workspace 130, including a number of spatial nodes 135each represented by a “+” symbol (only a couple are labeled). Multipledifferent workspaces may be created and defined for different patientwork areas. For example, workspace 130 may be spherical (as illustrated)and defined for treating VOIs residing within the head of a patient.Alternatively, workspace 130 may have other geometries (e.g.,elliptical) and defined for treating VOIs residing within other areas ofa patient. Additionally, multiple workspaces 130 may be defined fordifferent portions of a patient, each having different radius or sourceto axis distances (“SAD”), such as 650 mm and 800 mm. The SAD is thedistance between the electron target used for photon generation inradiation source 105 and the target described by the VOI. The SADdefines the surface area of the workspace. In one embodiment of anelliptical workspace, the SAD may range from 900 mm. to 1000 mm. OtherSADs may be used.

Spatial nodes 135 reside on the surface of workspace 130. Spatial nodes135 represent positions where radiation source 105 is allowed to stopand deliver a dose of radiation to the VOI within the patient. Duringdelivery of a treatment plan, robotic arm 125 moves radiation source 105to each and every spatial node 135 following a predefined path. In oneembodiment, even if a particular treatment plan does not call fordelivery of a dose of radiation from a particular spatial node 135,radiation source 105 will still visit that particular spatial node 135,since it falls along a predetermined safe path. In other embodiments therobot may skip unused nodes using more detailed knowledge of allowabletransitions between nodes.

FIG. 5 illustrates a complete node set including an exemplary number ofspatial nodes 135. The complete node set may include spatial nodes 135substantially uniformly distributed over the geometric surface ofworkspace 130. The complete node set includes all programmed spatialnodes 135 and provides a workable number of spatial nodes 135 foreffectively computing treatment plan solutions for most ailments andassociated VOIs. The complete node set provides a reasonably largenumber of spatial nodes 135 such that homogeneity and conformalitythresholds can be achieved for a large variety of different VOIs, whileproviding enough vantage points to avoid critical structures withinpatients. It will be appreciated that the complete node set may includemore or less spatial nodes 135 than is illustrated or discussed. Forexample, as processing power increases and experience gained creatingtreatment plans, the average number of spatial nodes 135 may increasewith time to provide greater flexibility and higher quality treatmentplans. In some embodiments, targets may have pre-defined spatial nodesets based on their location. The sets are typically discovered throughexperience with similar targets in the same or similar locations.

FIG. 6 illustrates re-orientation of the radiation source 105 at a node.As explained above, the radiation source 105 can be positioned at any ofthe spatial nodes 135. In addition, at each node, the radiation sourcecan be reoriented. For example, the radiation source 105 may bepositioned at a first orientation (orientation 1) at an angle α₁ at thenode 135. The radiation source 105 may also be reoriented to any numberof orientations at angle αN at the same node 135. In one embodiment, theradiation source 105 can be reoriented to twelve different orientationsat each node 135 (at twelve different angles α₁ . . . α₁₂). It will beappreciated that the radiation source 105 can be reoriented to fewerorientations or more orientations. As shown in FIG. 6, one orientation(orientation 1) may deliver a radiation beam at an angle that passesthrough the center of the VOI. Other orientations may deliver radiationbeams within the VOI, but not through the center of the VOI, and stillother orientations may deliver radiation beams outside of the VOI. Itwill be appreciated that the treatment planning system may automaticallyeliminate the orientations that deliver radiation beams outside of theVOI.

FIGS. 7A-7C illustrate exemplary algorithms for generating a treatmentplan for use in a treatment planning system. In one embodiment, thealgorithm is an iterative algorithm that optimizes deviations above themaximum dose constraint and below the minimum dose constraint. Theiterative planning algorithm first generates a set of candidate beamsand performs an initial dose distribution calculation, and subsequentlyattempts to improve the initial dose distribution calculation byaltering the weight of one or more beams. In another embodiment, thealgorithm performs convex optimization, such as, for example, theSimplex algorithm. One example of a cost function that may be optimizedby convex optimization is the number of monitor units (linearly relatedto the total amount of time for which the treatment beam enabled)subject to the minimum/maximum dose constraints. The Simplex algorithmis well-known in the art. Alternatively, other iterative andnon-iterative optimization algorithms may be used. In one embodiment, acombination of both algorithms may be used. In any event, the targetdelineation by the user is converted into a VOI bit mask (i.e., anoverlay on the 3D image volume used for delineation, such that eachposition with the 3D image has a bit representing each VOI, set to ‘1’if the given VOI overlaps that image position, and ‘0’ if it does not)for use with the treatment planning algorithm.

Typically, the treatment planning algorithms require targetidentification by the user. The treatment planning algorithm typicallypresents the user with a stack of 2D images which combine to representthe patient's 3D treatment area, and requires the user to identifycontours on the 2D images which are then combined to define the 3Dtarget volume (target VOI). In one embodiment, target identificationincludes a combination of edge detection and conversion of the edge to aseries of points in image space. This series of points may then becombined to generate a 3D structure which is rendered on top of a 3Dimage. Edge detection is described in further detail in Delp et al.,“Edge Detection Using Contour Tracing,” Center for Robotics andIntegrated Manufacturing, Robot System Division, College of Engineering,University of Michigan RSD-TR-12-83 (1983) 43. Contouring of points isdescribed in further detail in Mat, Ruzinoor Che, “Evaluation ofSilhouette Rendering Algorithms in Terrain Visualisation,” MSC ComputerGraphics and Virtual Environment Dissertation, Computer ScienceDepartment, The University of Hull(http:staf.uum.edu.my/ruzinoor/dissertation.htm). Other well-knownmethods for target identification may be used in the treatment planningalgorithms.

FIG. 7A shows a process 200 for generating a treatment plan. In theimplementation illustrated in FIG. 7A, the process 200 begins bydelineating a target VOI (block 205). In the implementation of FIG. 7A,the user identifies the target, and the system creates the target VOI(block 210). For brevity, we hereafter refer to this process as “theuser identifying the target VOI”, and similarly for the user identifyingthe critical structure VOIs.

The process 200 continues at block 215 by identifying dose constraints.The dose constraints include, for example but not limited to: minimumtarget VOI dose, maximum allowable dose to healthy tissue, degree ofhomogeneity, degree of conformality, total beam on time, a total numberof monitor units and a number of beams. In the implementation of FIG.7A, the user also identifies the dose constraints (block 220).Alternatively, a user may first identify dose constraints and thenidentify the target VOI, or the user may identify some dose constraints,identify the target VOI, and then identify other dose constraints.

The process 200 continues at block 225 where the user manually selectsthe beam shape and beam size. It will be appreciated that by manuallyselecting the beam shape and beam size, the user is manually selectingthe collimator(s) to be used in the treatment delivery. The beamorientation is randomly determined by the treatment planning algorithm.The treatment planning algorithm may use a random number generator incombination with the VOI bit mask to identify orientations which resultin a beam will intersect an internal or surface point in the VOI.

The process continues at block 230 where a dose mask is generated forcandidate beams. A dose mask is a representation of the amount ofradiation dose delivered by the beam to a set of locations in space,normalized to the duration of the beam. One example element in a dosemask would be a voxel location, say (128, 203, 245) in a CT image of thepatient, and a dose value of 1 cGy per second of beam on time. Anywell-known process for generating a dose mask may be used. In theimplementation of FIG. 7A, the candidate beams are randomly generated(block 235). The treatment planning algorithm may use a random numbergenerator in combination with the number of available beams, sizes,positions, orientations, or combinations thereof to generate thecandidate beam set. At block 240, beam weights are optimized forcandidate beams. Any well-known process for optimizing beam weights maybe used. As discussed above, the dose calculation and/or beamoptimization may be an iterative, convex or combination algorithm.

The process 200 ends at block 245 where the treatment plan is generated.The treatment plan may be subsequently delivered to the patient using aradiation treatment system. In one embodiment, the radiation treatmentsystem is the radiation treatment system 100 described above withreference to FIG. 5.

FIG. 7B shows another process 300 for generating a treatment plan inaccordance with one embodiment of the invention. In the implementationillustrated in FIG. 7B, the process begins by identifying a target VOI(block 305). In the implementation of FIG. 7B, the user identifies thetarget VOI (block 310), as described above. The process continues atblock 315 by identifying dose constraints. The dose constraints include,for example but not limited to: minimum VOI dose, maximum allowable doseto healthy tissue, degree of homogeneity, degree of conformality, totalbeam on time, a total number of monitor units and a number of beams. Inthe implementation of FIG. 7B, the user also identifies the doseconstraints (block 320). Alternatively, a user may first identify doseconstraints and then identify the target VOI, or the user may identifysome dose constraints, identify the target VOL, and then identify otherdose constraints.

The process continues at block 325 where one or more beam parameters areautomatically determined. In one embodiment, the beam parameter(s)include, for example, one or more of the beam orientation, beam shapeand beam size. Exemplary algorithms for automatically determining theone or more beam parameters are disclosed hereinafter. It will beappreciated that because the treatment planning algorithm automaticallydetermines the beam parameter(s), the treatment planning algorithm canalso automatically select one or more collimator sizes in order to bestsatisfy the dose constraints that have been applied. In one embodiment,the collimator(s) are fixed aperture collimator(s). In anotherembodiment, the collimator(s) are iris collimator(s). With an iriscollimator, the shape of the collimator aperture is fixed, but the sizeof the aperture may be varied during the treatment session, eithercontinuously or in fixed increments of size. In one embodiment, the IRIScollimator may be an IRIS collimator being developed by DeutschesKrebsforschungszentrum (DKFZ, German Cancer Research Center in theHelmholtz Association) of Heidelberg, Germany.

The process continues at block 330 where a dose mask is generated forcandidate beams. Any well-known process for generating a dose mask maybe used. In the implementation of FIG. 7B, the candidate beams aredetermined using the beam parameter(s) determined at block 325. Thecandidate beams may also be determined using the dose constraints andVOI bit mask. At block 340, beam weights are optimized for the candidatebeams. Any well-known process for optimizing beam weights may be used.As described above, the dose calculation arid/or beam optimization maybe an iterative, convex or combination algorithm.

The process 300 ends at block 345 where the treatment plan is generated.The treatment plan may be subsequently delivered to the patient using aradiation treatment system. In one embodiment, the radiation treatmentsystem is the radiation treatment system 100 described above withreference to FIG. 5.

FIG. 7C shows an iterative process 400 for automatically determining oneor more beam parameter(s) in accordance with one embodiment of theinvention. As shown in FIG. 7C, the process 400 determines at block 405if a node needs to be analyzed. The nodes referred to in the process ofFIG. 7C may be the spatial nodes 135 from FIG. 5. If a node needs to beanalyzed (block 405), the target silhouette is identified at block 410,the dose constraints are determined at block 420, the shape and/or sizeare automatically determined using the geometry of the target at block430, the orientation and/or size are automatically determined using apacking algorithm at block 440. The process returns to block 405 andrepeats itself at each node until no nodes remain. When no nodes remain,the process continues to block 450 where the dose mask is generated.

Exemplary processes for determining shape and/or size using the targetgeometry and exemplary processes for determining orientation and/or sizeusing a packing algorithm are disclosed hereinafter. It will also beappreciated that the iterative process of FIG. 7C may include fewersteps or more steps. For example, the iterative process may only includeautomatically determining one or more of the beam orientation, shape andsize at each node. It will also be appreciated that the order of stepsin the iterative process may vary. For example, the orientation and/orsize may be determined using the packing algorithm before the shapeand/or size are determined using the target geometry.

It will also be appreciated that the treatment planning algorithm mayinclude a combination of user selection (FIG. 7A) and automaticdetermination (FIGS. 7B and 7C). For example, the user may manuallyselect the beam size and beam shape, but the treatment planningalgorithm automatically determines the beam orientation. In anotherexample, the user manually selects the beam shape, and the treatmentplanning algorithm automatically determines the beam size and beamorientation. In addition, due to system constraints, the number ofcollimators may be fixed. Similarly, the collimator sizes may be fixed(e.g., a single collimator size) or restricted to a discrete set ofsizes. Configurations having continuously variable-sized beams may berounded to a nearest allowed collimator size(s).

As explained above with reference to FIGS. 7B and 7C, the treatment planmay include automatically determining one or more beam parameters. FIGS.8A-10B illustrate an aspect of the exemplary algorithms forautomatically determining beam parameter(s).

FIGS. 8A-8K illustrate an aspect of exemplary processes forautomatically determining a beam parameter using a packing algorithm.The object used to pack the VOI in the packing algorithm of theradiation treatment planning system corresponds to a cross section of aradiation beam. The radiation beam, in turn, corresponds to theradiation profile produced by one or more collimator(s). Thus, thepacking object defines one or more beam parameters. The beamparameter(s) can be used to automatically select one or morecollimators. For example, the size of the packing object may define thesize of the collimator, and the shape of the packing object may definethe shape of the collimator. Similarly, the center of the packing shapemay define the orientation of the collimator, with the orientation beingdefined by taking the line from the node to the center of the packingshape.

Packing algorithms, such as penny packing (for circles of equal size) orcircle packing (for circles of varying size) algorithms, produce a setof circles that best fill an object, such as a target silhouette withnon-overlapping circles. FIG. 8A shows a target (VOI) 500 havingmultiple circles 505 arranged in the VOI 500 according to a pennypacking algorithm with no overlap allowed. Alternative packingalgorithms find a set of overlapping circles whose union in the object.FIG. 8B illustrates an overlapping penny packing algorithm. In FIG. 8B,the circles 505 are arranged in the target 500 such that at least aportion of each circles overlaps another circle. It will be appreciatedthat the degree of overlap may vary from that shown in FIG. 8B.Exemplary circle packing algorithms are described at Collins et al., “Acircle packing algorithm,” Computational Geometry 25 (2003) 233-356, andChen et al., “Algorithms for Congruent Sphere Packing and Applications,”SCG '01 (2001) 212-221. Alternatively, other packing algorithms known inthe art may be used.

The circles (or other packing objects) may be a fixed size or multiplesizes. FIG. 8C illustrates that packing objects of different sizes maybe used by the packing algorithm. FIG. 8C shows the VOI 500 having acircle 510 having a first size, circles 515 having a second size andcircles 520 having a third size. In FIG. 8C, circle 510 is larger thancircles 515, which are larger than circles 520. It will be appreciatedthat fewer than three or greater than three sizes may be used by thepacking algorithm and that the size may vary from the sizes illustrated.

The size of the objects used in the packing algorithm may be determinedby examining the cross section of the predicted dose distribution (e.g.,as represented by a dose mask) for a given collimator size. For example,taking the cross section of the dose mask for a beam with 30 mmcollimator diameter, and taking all elements in the cross section havinga value of more than 1 cGy/second may give an approximation to a circlewith radius 15 mm.

As explained above, the packing algorithm may be an overlappingalgorithm. Medial axis transformation is an exemplary overlappingpacking algorithm. A medial axis transformation is a locus of centers ofmaximal inscribed disks. A maximal inscribed disk is a disk with aradius equal to the distance to the nearest boundary point that is notfully contained in any other inscribed disk centered at any other pointin the object. The union of the set of all maximal inscribed disks isthe object itself (i.e., the VOI). The skeleton plus the radii of themaximal disks at all skeleton points is a symmetric axis transform. Anexemplary medial axis transformation algorithm is described at Ge etal., “On the Generation of Skeletons from Discrete Euclidean DistanceMaps.” IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 18, No. 11 (1996) 1055-1066. Alternatively, other medial axistransformation algorithms or non-overlapping algorithms known in the artmay be used.

FIGS. 8D and 8E illustrate medial axis transformation with a VOI. FIG.8D shows a VOI 500 having an irregular geometry with a skeleton 525therein, formed using a medial axis transformation algorithm. FIG. 8Eillustrates the medial axis transformation algorithm with a simpletarget geometry. It will be appreciated that medial axis transformationalgorithms may be used with more complex target geometry as well; asimple target geometry is merely used for ease of description. In FIG.8E, the VOI 500 a includes a skeleton 525 a. Circles 505 a are arrangedalong the skeleton 525 a. The skeleton 525 a is used to determine theset of possible circles 505 a. The algorithm, based on the doseconstraints, then decides which of those circles 505 a can be used tosatisfy the dose constraints. For example, if the algorithm identifies100 circles 505 a, the algorithm may only pick five of the circles 505a, and hence corresponding collimator sizes and orientations, fortreatment purposes. In addition, a maximum amount of overlap can beidentified, and/or a maximum amount of uncovered area can be defined bythe user or calculated based on the dose constraints, such ashomogeneity, maximum dose amount and conformality, to eliminate some ofthe circles 505 a.

FIG. 8F shows a VOI 500 having a first outline of the target silhouette530 and a second outline of the target silhouette 540. The first outlineof the target silhouette 530, as opposed to the actual silhouette 500,can be used by the packing algorithm if the user desires, for example,conformality. The second outline of the target silhouette 540, asopposed to the actual silhouette 500, can be used by the packingalgorithm if the user desires, for example, dose homogeneity.

FIG. 8G illustrates the application of erosion and dilation of a beam toa packing algorithm. In FIG. 8G, the VOI 500 b includes circles 505 b,each circle having a first outline of the circle 530 b and a secondoutline of the circle 540 b. The first outline 530 b corresponds toerosion and the second outline 540 b corresponds to dilation of theradiation beam. Erosion and dilation allow overlapping packingalgorithms to become non-overlapping algorithms and non-overlappingalgorithms to become overlapping algorithms, respectively.

FIG. 8H-K illustrate packing algorithms with packing objects havingdifferent shapes and combinations of shapes. In one embodiment, theshape of the packing object is a geometric primitive (i.e., the shape ofthe collimator is a geometric primitive). Exemplary geometric primitivesinclude, for example, circles, ellipses, hexagons, regular polygons andirregular polygons (e.g., a trapezium).

FIG. 8H shows a VOI 500 packed with ellipses 500, corresponding to anelliptically shaped radiation beam (i.e., elliptically shapedcollimator). FIG. 8I shows a VOI 500 packed with a circle 555 andellipses 560. FIG. 8j shows the target 500 packed with hexagons 565.FIG. 8K shows the target 500 packed with a hexagon 570, ellipses 575 andcircles 580. It will be appreciated that the types of shapes,combinations of shapes, etc., used in the treatment planning algorithmmay vary from those illustrated in FIGS. 8H-8K.

As shown in FIGS. 8A-8H, the use of collimator(s) of different sizesand/or shapes and/or at different orientations can be particularlyadvantageous with irregularly shaped targets. For example, a largecollimator can deliver dose rapidly to the central part of the targetwhile smaller collimators can deliver dose to conform to the irregularshape of the periphery. In addition, the use of collimators) ofdifferent sizes and/or shapes and/or at different orientations canresult in more effective treatment planning.

FIGS. 9A and 9B illustrate exemplary algorithms in which one or morebeam parameters are automatically determined using the geometry of thetarget (VOI) 600. An exemplary algorithm is disclosed in Alpert et al.,“The Principal Axes Transformation—A Method for Image Registration.” JNucl Med 1990; 31:1717-1722. As discussed above, the beam parameterslead to the selection of one or more collimators. The collimator may beselected as a function of a characteristic geometric dimension and/or acharacteristic measure of shape. Various measures of shape can be used,including the ratio of minimum and maximum principal axis, variousmeasures of eccentricity, and surface-to-volume ratio (with or withoutnormalization to the surface-to-volume ratio of a sphere of identicalvolume).

FIG. 9A shows the VOI 600 having a center of mass 605. A collimator isshown in the center of the target 600 at the center of mass 605. Acoordinate system 615 is shown, originating from the center of mass 605.In one embodiment, the collimator is selected as a specific percentageof a characteristic geometric dimension. For example, the primary axes(principal axes) of the user-delineated target are determined, and thecollimator is selected as a specific percentage of the smallestprincipal axis. In the illustrated embodiment, the principal axes arerepresented by the coordinate system 615 and the smallest principal axisis represented by the axis 620. In one embodiment, the collimator sizemay be 100%-200% of the smallest principal axis. It will be appreciatedthat the collimator size may also be less than 100% of the smallestprincipal axis.

FIG. 9B shows an axis 625 through the center of the target 600. Aplurality of axes 630 are shown perpendicular to the axis 625. In oneembodiment, the axes 630 are used in a root mean square analysis of thetarget 625. The root mean square analysis may be useful in identifying abeam size.

The treatment planning algorithm analyzes the VOI from each nodeposition to find the one or more collimator sizes such that geometricprimitives (i.e., packing object shape) of one or more characteristicsizes (e.g., circles of one or more diameters), corresponding to theavailable collimators, optimally fill or pack the VOI subject to thedose constraints. FIGS. 10A and 10B show a target (VOI) from twodifferent node positions. FIG. 10A shows the VOI 700 a from a firstposition, and FIG. 10B shows the VOI 700 b from a second position. Thesame VOI has different shapes depending on the position. Both VOIs 700 aand 700 b are shown packed with circles 705, but the VOI 700 b is moreefficiently packed than the VOI 700 a. As described above, the shape ofthe packing object and its size correspond to the collimator shape andsize, and its position in the VOI corresponds to the beam orientationused to generate the candidate beams at each node position.

FIGS. 11A-E are exemplary screen shots of a user interface 800 for atreatment planning system. It will be appreciated that the userinterface and screen shots may vary from those illustrated anddescribed. As shown in FIG. 11A, images of the treatment region areloaded into the treatment planning system. FIG. 11B shows different 2Dimage slices containing cross sections of the target. As shown in FIG.11C, the user may enter various dose constraints, as described above,into the user interface 800. FIG. 11D shows a treatment plan for thetarget generated using an algorithm described herein. FIG. 11E shows atreatment plan for the target, in which the collimator sizes areautomatically selected. Alternatively, the user may be presented withsuggested collimator size(s), and can accept and/or modify the suggestedcollimator size(s).

It should be noted that embodiments of the present invention may be usedwith either, or both, forward and inverse planning techniques (e.g.,isocentric and non-isocentric, or conformal, beam geometries) to developa treatment plan. FIG. 12 illustrates a two-dimensional perspective ofnon-isocentric radiation beam delivery at a target region based onconformal planning. It should be noted that four beams, beam_1 901,beam_2 902, beam_3 903, and beam_4 904 are illustrated in FIG. 12 onlyfor ease of discussion and that an actual treatment plan may includemore, or fewer, than four beams. Moreover, the four beams arerepresentative of conformal planning, in which each beam passes throughvarious points within target region 900 (e.g., the pathologicalanatomy). In conformal planning, some beams may or may not intersect orconverge at a common point, and although the four beams appear tointersect in the perspective of FIG. 12, the beams may not intersect intheir actual three-dimensional space. The radiation beams need onlyintersect with the target volume and do not necessarily converge on asingle point, or isocenter, within the target 900. In one embodiment,conformal planning takes advantage of an image-guided, robotic-basedradiation treatment system (e.g., for performing radiosurgery) such asthe CyberKnife® system, because the LINAC positioning mechanism (e.g.,robotic arm 3012 of FIG. 14) can move around freely with multipledegrees of freedom, allowing the radiation beams of the LINAC to pointanywhere in space.

FIG. 13 illustrates one embodiment of systems that may be used toperform radiation treatment in which features of the present inventionmay be implemented. As described below and illustrated in FIG. 13,system 4000 may include a diagnostic imaging system 1000, a treatmentplanning system 2000, and a treatment delivery system 100. Diagnosticimaging system 1000 may be any system capable of producing medicaldiagnostic images of a treatment region in a patient that may be usedfor subsequent medical diagnosis, treatment planning and/or treatmentdelivery. For example, diagnostic imaging system 1000 may be a computedtomography (CT) system, a magnetic resonance imaging (MRI) system, apositron emission tomography (PET) system, an ultrasound system or thelike. For ease of discussion, diagnostic imaging system 1000 may bediscussed below at times in relation to a CT x-ray imaging modality.However, other imaging modalities such as those above may also be used.

Diagnostic imaging system 1000 includes an imaging source 1010 togenerate an imaging beam (e.g., x-rays, ultrasonic waves, radiofrequency waves, etc.) and an imaging detector 1020 to detect andreceive the beam generated by imaging source 1010, or a secondary beamor emission stimulated by the beam from the imaging source (e.g., in anMRI or PET scan). In one embodiment, diagnostic imaging system 1000 mayinclude two or more diagnostic X-ray sources and two or morecorresponding imaging detectors. For example, two x-ray sources may bedisposed around a patient to be imaged, fixed at an angular separationfrom each other (e.g., 90 degrees, 45 degrees, etc.) and aimed throughthe patient toward (an) imaging detector(s) which may be diametricallyopposed to the x-ray sources. A single large imaging detector, ormultiple imaging detectors, may also be used that would be illuminatedby each x-ray imaging source. Alternatively, other numbers andconfigurations of imaging sources and imaging detectors may be used.

The imaging source 1010 and the imaging detector 1020 are coupled to adigital processing system 1030 to control the imaging operation andprocess image data. Diagnostic imaging system 1000 includes a bus orother means 1035 for transferring data and commands among digitalprocessing system 1030, imaging source 1010 and imaging detector 1020.Digital processing system 1030 may include one or more general-purposeprocessors (e.g., a microprocessor), special purpose processor such as adigital signal processor (DSP) or other type of device such as acontroller or field programmable gate array (FPGA). Digital processingsystem 1030 may also include other components (not shown) such asmemory, storage devices, network adapters and the like. Digitalprocessing system 1030 may be configured to generate digital diagnosticimages in a standard format, such as the DICOM (Digital Imaging andCommunications in Medicine) format, for example. In other embodiments,digital processing system 1030 may generate other standard ornon-standard digital image formats. Digital processing system 1030 maytransmit diagnostic image files (e.g., the aforementioned DICOMformatted files) to treatment planning system 2000 over a data link1500, which may be, for example, a direct link, a local area network(LAN) link or a wide area network (WAN) link such as the Internet. Inaddition, the information transferred between systems may either bepulled or pushed across the communication medium connecting the systems,such as in a remote diagnosis or treatment planning configuration. Inremote diagnosis or treatment planning, a user may utilize embodimentsof the present invention to diagnose or treatment plan despite theexistence of a physical separation between the system user and thepatient.

Treatment planning system 2000 includes a processing device 2010 toreceive and process image data. Processing device 2010 may represent oneor more general-purpose processors (e.g., a microprocessor), specialpurpose processor such as a digital signal processor (DSP) or other typeof device such as a controller or field programmable gate array (FPGA).Processing device 2010 may be configured to execute instructions forperforming the operations of the treatment planning system 2000discussed herein that, for example, may be loaded in processing device2010 from storage 2030 and/or system memory 2020.

Treatment planning system 2000 may also include system memory 2020 thatmay include a random access memory (RAM), or other dynamic storagedevices, coupled to processing device 2010 by bus 2055, for storinginformation and instructions to be executed by processing device 2010.System memory 2020 also may be used for storing temporary variables orother intermediate information during execution of instructions byprocessing device 2010. System memory 2020 may also include a read onlymemory (ROM) and/or other static storage device coupled to bus 2055 forstoring static information and instructions for processing device 2010.

Treatment planning system 2000 may also include storage device 2030,representing one or more storage devices (e.g., a magnetic disk drive oroptical disk drive) coupled to bus 2055 for storing information andinstructions. Storage device 2030 may be used for storing instructionsfor performing the treatment planning methods discussed herein.

Processing device 2010 may also be coupled to a display device 2040,such as a cathode ray tube (CRT) or liquid crystal display (LCD), fordisplaying information (e.g., a two-dimensional or three-dimensionalrepresentation of the VOI) to the user. An input device 2050, such as akeyboard, may be coupled to processing device 2010 for communicatinginformation and/or command selections to processing device 2010. One ormore other user input devices (e.g., a mouse, a trackball or cursordirection keys) may also be used to communicate directional information,to select commands for processing device 2010 and to control cursormovements on display 2040.

It will be appreciated that treatment planning system 2000 representsonly one example of a treatment planning system, which may have manydifferent configurations and architectures, which may include morecomponents or fewer components than treatment planning system 2000 andwhich may be employed with the present invention. For example, somesystems often have multiple buses, such as a peripheral bus, a dedicatedcache bus, etc. The treatment planning system 2000 may also includeMIRIT (Medical Image Review and Import Tool) to support DICOM import (soimages can be fused and targets delineated on different systems and thenimported into the treatment planning system for planning and dosecalculations), expanded image fusion capabilities that allow the user totreatment plan and view dose distributions on any one of various imagingmodalities (e.g., MRI, CT, PET, etc.). Treatment planning systems areknown in the art; accordingly, a more detailed discussion is notprovided.

Treatment planning system 2000 may share its database (e.g., data storedin storage device 2030) with a treatment delivery system, such astreatment delivery system 100, so that it may not be necessary to exportfrom the treatment planning system prior to treatment delivery.Treatment planning system 2000 may be linked to treatment deliverysystem 100 via a data link 2500, which may be a direct link, a LAN linkor a WAN link as discussed above with respect to data link 1500. Itshould be noted that when data links 1500 and 2500 are implemented asLAN or WAN connections, any of diagnostic imaging system 1000, treatmentplanning system 2000 and/or treatment delivery system 100 may be indecentralized locations such that the systems may be physically remotefrom each other. Alternatively, any of diagnostic imaging system 2000,treatment planning system 2000 and/or treatment delivery system 100 maybe integrated with each other in one or more systems.

Treatment delivery system 100 includes a therapeutic and/or surgicalradiation source 105 to administer a prescribed radiation dose to atarget volume in conformance with a treatment plan. Treatment deliverysystem 100 may also include an imaging system 3020 to captureintra-treatment images of a patient volume (including the target volume)for registration or correlation with the diagnostic images describedabove in order to position the patient with respect to the radiationsource. Treatment delivery system 100 may also include a digitalprocessing system 3030 to control radiation source 105, imaging system3020, and a patient support device such as a treatment couch 110.Digital processing system 3030 may include one or more general-purposeprocessors (e.g., a microprocessor), special purpose processor such as adigital signal processor (DSP) or other type of device such as acontroller or field programmable gate array (FPGA). Digital processingsystem 3030 may also include other components (not shown) such asmemory, storage devices, network adapters and the like. Digitalprocessing system 3030 may be coupled to radiation source 105, imagingsystem 3020 and treatment couch 110 by a bus 3045 or other type ofcontrol and communication interface.

In one embodiment, as illustrated in FIG. 14, treatment delivery system100 may be an image-guided, robotic-based radiation treatment system(e.g., for performing radiosurgery) such as the CyberKnife® systemdeveloped by Accuray Incorporated of California. In FIG. 14, radiationsource 105 may be represented by a linear accelerator (LINAC) mounted onthe end of a robotic arm 3012 having multiple (e.g., 5 or more) degreesof freedom in order to position the LINAC to irradiate a pathologicalanatomy (target region or volume) with beams delivered from many anglesin an operating volume (e.g., a sphere) around the patient. Treatmentmay involve beam paths with a single isocenter (point of convergence),multiple isocenters, or with a non-isocentric approach (i.e., the beamsneed only intersect with the pathological target volume and do notnecessarily converge on a single point, or isocenter, within the targetas illustrated in FIG. 12). Treatment can be delivered in either asingle session (mono-fraction) or in a small number of sessions asdetermined during treatment planning. With treatment delivery system100, in one embodiment, radiation beams may be delivered according tothe treatment plan without fixing the patient to a rigid, external frameto register the intra-operative position of the target volume with theposition of the target volume during the pre-operative treatmentplanning phase.

In FIG. 14, imaging system 3020 may be represented by X-ray sources 120Aand 120B and X-ray image detectors (imagers) 115A and 115B. In oneembodiment, for example, two x-ray sources 120A and 120B may benominally aligned to project imaging x-ray beams through a patient fromtwo different angular positions (e.g., separated by 90 degrees, 45degrees, etc.) and aimed through the patient on treatment couch 110toward respective detectors 115A and 115B. In another embodiment, asingle large imager can be used that would be illuminated by each x-rayimaging source. Alternatively, other numbers and configurations ofimaging sources and imagers may be used.

Digital processing system 3030 may implement algorithms to register(i.e., determine a common coordinate system for) images obtained fromimaging system 3020 with pre-operative treatment planning images inorder to align the patient on the treatment couch 110 within thetreatment delivery system 100, and to precisely position the radiationsource with respect to the target volume.

The treatment couch 110 may be coupled to another robotic arm (notillustrated) having multiple (e.g., 5 or more) degrees of freedom. Thecouch arm may have five rotational degrees of freedom and onesubstantially vertical, linear degree of freedom. Alternatively, thecouch arm may have six rotational degrees of freedom and onesubstantially vertical, linear degree of freedom or at least fourrotational degrees of freedom. The couch arm may be vertically mountedto a column or wall, or horizontally mounted to pedestal, floor, orceiling. Alternatively, the treatment couch 110 may be a component ofanother mechanical mechanism, such as the Axum® treatment couchdeveloped by Accuray Incorporated of California, or be another type ofconventional treatment table known to those of ordinary skill in theart.

It should be noted that the methods and apparatus described herein arenot limited to use only with medical diagnostic imaging and treatment.In alternative embodiments, the methods and apparatus herein may be usedin applications outside of the medical technology field, such asindustrial imaging and non-destructive testing of materials (e.g., motorblocks in the automotive industry, airframes in the aviation industry,welds in the construction industry and drill cores in the petroleumindustry) and seismic surveying. In such applications, for example,“treatment” may refer generally to the effectuation of an operationcontrolled by the treatment planning system, such as the application ofa beam (e.g., radiation, acoustic, etc.).

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

1. A method comprising: receiving identification of a target and doseconstraints in a treatment planning algorithm for a radiation beamtreatment system; and automatically determining a size of a radiationbeam for the radiation beam treatment system using a geometry of thetarget or a packing algorithm.
 2. The method of claim 1, wherein thedose constraints include degree of homogeneity.
 3. The method of claim1, wherein the dose constraints include degree of conformality.
 4. Themethod of claim 1, wherein the dose constraints include a minimum dose.5. The method of claim 1, wherein the dose constraints include a maximumdose.
 6. The method of claim 1, wherein the dose constraints include atotal beam on time.
 7. The method of claim 1, wherein the total beam ontime is determined by a total number of monitor units.
 8. The method ofclaim 1, wherein the dose constraints include a number of beams.
 9. Themethod of claim 1, wherein the size is selected as a percentage of acharacteristic geometric dimension of the target.
 10. The method ofclaim 1, wherein determining the size comprises determining a target tobe radiated and identifying principal axes of the target, wherein thesize is a percentage of a smallest principal axis of the target.
 11. Themethod of claim 1, wherein determining the size comprises identifying atarget silhouette of the target and packing the target silhouette withan object corresponding to a radiation beam of the radiation treatmentsystem.
 12. The method of claim 11, wherein the beam size is the size ofthe object.
 13. The method of claim 1, wherein the size is selected as afunction of a characteristic geometric dimension and a characteristicmeasure of shape of the target.
 14. The method of claim 13, wherein thecharacteristic measure of shape is a ratio of minimum and maximumprincipal axes of the target.
 15. The method of claim 13, wherein thecharacteristic measure of shape is a measure of eccentricity of thetarget.
 16. The method of claim 13, wherein the characteristic measureof shape a surface-to-volume ratio of the target.
 17. The method ofclaim 1, wherein determining a size comprises determining a first sizefor a radiation beam and determining a second size for a radiation beam.18. The method of claim 17, wherein the first size is different from thesecond size.
 19. The method of claim 1, wherein determining a sizecomprises determining a first optimal size and a second optimal size,presenting the first and second optimal size to a user, and receiving auser selection of the first optimal size or the second optimal size. 20.The method of claim 1, further comprising selecting a collimator havingthe beam shape.
 21. The method of claim 20, wherein the collimator is afixed aperture collimator.
 22. The method of claim 20, wherein thecollimator is an iris collimator.
 23. A method comprising: receivingdose constraints in a treatment planning algorithm for a radiation beamtreatment system; and automatically determining a beam shape for theradiation beam treatment system using a geometry of the target or apacking algorithm.
 24. The method of claim 23, wherein determining theshape comprises identifying a target silhouette of the target andpacking the target silhouette with an object corresponding to aradiation beam of the radiation treatment system.
 25. The method ofclaim 24, wherein the beam shape is the shape of the object.
 26. Themethod of claim 52, wherein the size of the object is determined byexamining a cross section of the beam dose distribution.
 27. The methodof claim 23, wherein the packing algorithm is a non-overlappingalgorithm.
 28. The method of claim 23, wherein the packing algorithm isan overlapping algorithm.
 29. The method of claim 23, wherein thepacking algorithm is a penny packing algorithm.
 30. The method of claim23, wherein the packing algorithm is a circle packing algorithm.
 31. Themethod of claim 23, wherein the packing algorithm comprises medial axistransformation.
 32. The method of claim 23, wherein the packingalgorithm comprises packing with beams having different sizes.
 33. Themethod of claim 23, wherein the beam shape is a geometric primitive. 34.The method of claim 33, wherein the packing algorithm comprises packingwith the geometric primitive.
 35. The method of claim 33, wherein thegeometric primitive includes a circle.
 36. The method of claim 33,wherein the geometric primitive includes an ellipse.
 37. The method ofclaim 33, wherein the geometric primitive is a regular polygon orirregular polygon.
 38. The method of claim 23, further comprisingautomatically calculating a dose mask.
 39. The method of claim 23,wherein the dose constraints include degree of homogeneity.
 40. Themethod of claim 23, wherein the dose constraints include degree ofconformality.
 41. The method of claim 23, wherein the dose constraintsinclude a minimum dose.
 42. The method of claim 23, wherein the doseconstraints include a maximum dose.
 43. The method of claim 23, whereinthe dose constraints include total beam on time.
 44. The method of claim23, wherein the dose constraints include a total number of monitorunits.
 45. The method of claim 23, wherein the dose constraints includea number of beams.
 46. The method of claim 23, further comprisingselecting a collimator having the beam shape.
 47. The method of claim46, wherein the collimator is a fixed aperture collimator.
 48. Themethod of claim 46, wherein the collimator is an iris collimator.
 49. Asystem comprising: means for receiving identification of a target anddose constraints in a treatment planning algorithm for a radiation beamtreatment system; and means for automatically determining a size of aradiation beam for the radiation beam treatment system using a geometryof the target or a packing algorithm.
 50. The system of claim 49,wherein the size is selected as a percentage of a characteristicgeometric dimension of the target.
 51. The system of claim 49, whereinthe dose constraints include degree of homogeneity.
 52. A systemcomprising: means for receiving dose constraints in a treatment planningalgorithm for a radiation beam treatment system; and means forautomatically determining a beam shape for the radiation beam treatmentsystem using a geometry of the target or a packing algorithm.
 53. Thesystem of claim 52, wherein the beam shape is a geometric primitive. 54.The system of claim 52, further comprising means for selecting acollimator corresponding to the determined beam shape.
 55. An apparatuscomprising: a radiation beam treatment system to deliver a radiationbeam to a treatment site; and a radiation treatment planning systemoperatively coupled to the radiation beam treatment system, theradiation treatment planning system to receive identification of atarget and dose constraints in a treatment planning algorithm for theradiation beam treatment system and automatically determine a size of aradiation beam for the radiation beam treatment system using a geometryof the target or a packing algorithm.
 56. The apparatus of claim 55,wherein the dose constraints include total beam on time of the radiationbeam.
 57. The apparatus of claim 55, wherein the dose constraintsinclude a number of beams.
 58. An apparatus comprising: a radiation beamtreatment system to deliver a radiation beam to a treatment site; and aradiation treatment planning system operatively coupled to the radiationbeam treatment system, the radiation treatment planning system toreceive dose constraints in a treatment planning algorithm for aradiation beam treatment system and automatically determine a beam shapefor the radiation beam treatment system using a geometry of the targetor a packing algorithm.
 59. The apparatus of claim 58, wherein the doseconstraints include total beam on time of the radiation beam.
 60. Theapparatus of claim 58, wherein the dose constraints include a number ofbeams delivered by the radiation treatment system.